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Burr detection by using vision image

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Abstract

In this paper, a practical force model for the deburring process is first presented. It will be shown that the force model is more general than Kazerooni's model and it is suitable for both upcut and down-cut grinding. In terms of this force model, an algorithm of burr detection by using a 2D vision image is proposed. In the burr detection algorithm, the relevant data of burrs, such as frequency, cross-section area, and height are simplified so that they are functions of the burr contour only. Then, a fast tracking method of the burr contour (BCTM) is developed to obtain the contour data. Experiments show that the BCTM of this passive (i.e. without lighting) image system can be as fast as 18.2 Hz and its precision is 0.02 mm, so online burr detection and control by using the vision sensor is feasible.

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Abbreviations

A burr :

cross-section area of the burr

A chamfer :

cross-section area of the chamfer

A n :

proportional factor

A work :

cross section area in the contact zone while deburringA work=A burr+A chamfer

w :

cutting width

w root :

thickness of the root of the burr

a :

depth of cut

a root :

burr heighta root=a(w root)

C 1 :

static cutting edge density

D :

equivalent wheel diameter

d s :

wheel diameter

d w :

workpiece diameterD=d w d s/(d w±d s)D=d s andd w→∞ for the deburring process

F h :

horizontal grinding force

F v :

vertical grinding force

F n :

normal grinding force

F t :

tangential grinding force

F n(K) :

normal grinding force of the Kazerooni's model

F t(K) :

tangential grinding force of the Kazerooni's model

F o :

threshold thrust force

f burr :

burr frequency

f n :

normal grinding force per active grain

f t :

tangential grinding force per active grain

f r :

first resonant frequency of the robot

f tool :

resonant frequency of the end-effector at the normal direction

ε:

exponential constant for describing the edge distribution ε = [(1 +n) + α(1 −n)]/2 ε = (1 +n)/2 for α = 0 [21]

K :

proportional factor of the force model of the grinding processK =A 1−n n /ε

K 0 :

specific contact force per contact length

K 1 :

specific chip formation force per contact length

V s :

wheel speed

V w :

workpiece speed

⋀w :

metal-removal parameter

K 2 :

specific metal-removal parameter per wheel speedK 2 = ⋀w/V s

K c :

specific chip formation force per area

K f :

specific friction force per area

k :

constant for the parabolic burr

k 1,k 2,k 3,k 4 :

constants for the circular burr

L :

contact width between the wheel and the workpieceL is equal to the chamfer's hypotenuse length, orL=w root when there is no chamfer

l :

contact length

l k :

contact length between the wheel and the workpiece

m :

exponential constant for describing the edge shape 0≤m≤1m=1 for the deburring process [21]

N dyn :

number of engaged cutting edges per wheel surface

n :

exponential constant for describing the cutting process 0≤n≤1n=1 for the pure chip formation process andn=0 for the pure friction process [22]

\(\bar P\) :

average contact pressure

p :

exponential constant for describing the relationship between the static cutting edge and the wheel surface depth 1≤p≤2p=1 for linear case [21]

Q :

magnitude of the individual chip cross-section in the contact zone

r :

radius of the circular burr

Z w :

metal-removal rate

α,β,γ:

exponential constants for describing the edge distribution [21] α = (p −m)/(p + 1) α = 0 form = 1,p = 1 β =p/(p) + 1 β = 1/2 forp = 1 γ = β(1 −n) γ = 1n/2 for β = 1/2

δ:

actual contact area between the wheel and the workpiece

μ:

coefficient of the sliding friction

θ:

variable of the contact angle

θk :

maximum contact angle

θm :

mean rotating angle

θt :

half of the tip angle of the grains

Φ:

ratio of tangential chip formation force to the normal chip formation force. Usuihideji has pointed out that Φ = π/(4tanθt) [29]

References

  1. C. Rubenstein, “The mechanics of grinding”,International Journal of Machine Tool Design and Research,12, pp. 127–139, 1972.

    Google Scholar 

  2. R. S. Hahn and R. P. Lindsay, “Principles of grinding”,Machinery Magazine, July–November 1971.

  3. G. Boothroyd,Fundamentals of Metal Machining and Machine Tools, Scripta Book Company, 1975.

  4. R. S. Hahn, “On the mechanics of the grinding process under plunge cut conditions”,Journal of Engineering for Industry, February 1966.

  5. H. Kazerooni, “Automated robotic deburring using electronic compliance: impedance control”,Proceedings of IEEE International Conference of Robotics and Automation, pp. 1025–1032, 1987.

  6. H. Kazerooni, J. J. Bausch and B. M. Kramer, “An approach to automated deburring by robot manipulators”,ASME Journal of Dynamic Systems, Measurement, and Control,108, pp. 354–359, 1986.

    Google Scholar 

  7. H. P. Huang, S. S. Lu and C. A. Shih, “Robotic deburring problems”,Proceedings of the 5th CSME National Conference on Mechanical Engineering, pp. 769–779, 1988.

  8. H. P. Huang, S. S. Lu and C. A. Shih, “Robot application to deburring problems I”,National Science Council, NSC 77-0611-E002-03, 1988.

  9. H. P. Huang, S. S. Lu, C. A. Shih, M. H. Lin and K. C. Lee, “Robot application to deburring problems II”,National Science Council, NSC 78-0422-E002-01, 1989.

  10. H. P. Huang, S. S. Lu, C. A. Shih and M. H. Lin, “Robot application to deburring problems III”,National Science Council, NSC 79-0422-E002-03, 1990.

  11. Z. Aviad and E. Lozinskii, “Semantic thresholding”,Pattern Recognition Letters,5, pp. 321–328, 1987.

    Google Scholar 

  12. A. D. Brink, “Grey level thresholding of images using a correlation criterion”,Pattern Recognition Letters,9, pp. 335–341, 1989.

    Google Scholar 

  13. K. S. Fu, R. C. Gonzales and C. S. G. Lee,Robotics: Control, Sensing, Vision and Intelligence, McGraw-Hill, New York, 1987.

    Google Scholar 

  14. J. F. Haddon, “Generalized threshold selection for edge detection”,Pattern Recognition,21 (3), pp. 195–203, 1988.

    Google Scholar 

  15. T. W. Ridler and S. Calvard, “Picture thresholding using an iterative selection method”,IEEE Transactions on Systems, Man, and Cybernetics SMC-8 (8), pp. 630–632, 1978.

    Google Scholar 

  16. A. Rosenfeld and A. C. Kak,Digital Picture Processing, Academic Press Incorporation, New York, 1976.

    Google Scholar 

  17. J. S. Weszka, R. N. Nagel and A. Rosenfeld, “A threshold selection technique”,IEEE Transactions on Computers, pp. 1322–1326, 1974.

  18. O. A. Zuniga and R. M. Haralick, “Gradient threshold selection using the facet model”,Pattern Recognition,21 (5), pp. 493–503, 1988.

    Google Scholar 

  19. K. C. Lee and H. P. Huang, “Robot vision sensory assembly cell”.Proceedings of the 7th CSME National Conference on Mechanical Engineering, pp. 1705–1712, 1990.

  20. Y. Nakagawa and A. Rosenfeld, “A note on the use of local min and max operations in digital picture processing”,IEEE Transactions on Systems, Man, and Cybernetics SMC-8 (8), pp. 632–635, 1978.

    Google Scholar 

  21. G. Werner, “Influence of work material on grinding forces”,Annals of the CIRP,27 (1), pp. 243–248, 1978.

    Google Scholar 

  22. K. H. Fuh and J. S. Huang, “The study of force model for creep feed grinding”,Proceedings of the 7th CSME National Conference on Mechanical Engineering, pp. 1583–1589, 1990.

  23. F. Furukawa, S. Ohishi and S. Shiozaki, “Selection of creep feed grinding conditions in view of workpiece burning”,Annals of the CIRP 28,1, pp. 213–218, 1979.

    Google Scholar 

  24. C. E. Davis, “The dependence of grinding wheel performance of dressing procedure”,International Journal of Machine Tool Design and Research,14, pp. 33–52, 1974.

    Google Scholar 

  25. T. El-Wardani, M. M. Sadek and Y. Younis, “Theoretical analysis of grinding chatter”,ASME Journal of Engineering for Industry,109, pp. 314–320, November 1987.

    Google Scholar 

  26. H. Asada and N. Goldfine, “Optimal compliance design for grinding robot tool holders”,Proceedings of IEEE International Conference of Robotics and Automation, pp. 316–322, March 1985.

    Google Scholar 

  27. J. J. Huang, “Top grinding technology”,Mechanical Technology Magazine, Taipei, 1988.

  28. S. Malkin,Grinding Technology, Ellis Horwood Limited, Chichester, England, 1989.

    Google Scholar 

  29. Usuihideji,Technology of Cutting and Grinding, Japan, 1971.

  30. D. E. Whitney, A. C. Edsall, A. B. Todtenkopt, T. R. Kurfess and A. R. Tate, “Development and control of an automated robotic weld bead grinding system”,ASME Journal of Dynamic Systems, Measurement, and Control,112, pp. 166–176, June 1990.

    Google Scholar 

  31. Y. Younis, M. M. Sadek and T. El-Wardani, “A new approach to development of a grinding force model”,ASME Journal of Engineering for Industry,109, pp. 306–313, November 1987.

    Google Scholar 

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Authors and Affiliations

  1. Roboties Laboratory, Department of Mechanical Engineering, National Taiwan University, No 1 Roosevelt Road, Sec. 4, 10764, Taipei, Taiwan, ROC

    Kuang-Chyi Lee, Han-Pang Huang & Shui-Shong Lu

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  1. Kuang-Chyi Lee
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Lee, KC., Huang, HP. & Lu, SS. Burr detection by using vision image. Int J Adv Manuf Technol 8, 275–284 (1993). https://doi.org/10.1007/BF01783611

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